Bibhas Chakraborty

Web-Based Smoking-Cessation Programs : Results of a Randomized Trial

BACKGROUND Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation. DESIGN Randomized fractional factorial design. SETTING Two HMOs: Group Health in Washington State and Henry Ford Health System in Michigan. PARTICIPANTS 1866 smokers. INTERVENTION A web-based smoking-cessation program plus nicotine patch. Five components of the intervention were randomized using a fractional factorial design: high- versus low-depth tailored success story, outcome expectation, and efficacy expectation messages; high- versus low-personalized source; and multiple versus single exposure to the intervention components. MEASUREMENTS Primary outcome was 7 day point-prevalence abstinence at the 6-month follow-up. FINDINGS Abstinence was most influenced by high-depth tailored success stories and a high-personalized message source. The cumulative assignment of the three tailoring depth factors also resulted in increasing the rates of 6-month cessation, demonstrating an effect of tailoring depth. CONCLUSIONS The study identified relevant components of smoking-cessation interventions that should be generalizable to other cessation interventions. The study also demonstrated the importance of higher-depth tailoring in smoking-cessation programs. Finally, the use of a novel fractional factorial design allowed efficient examination of the study aims. The rapidly changing interfaces, software, and capabilities of eHealth are likely to require such dynamic experimental approaches to intervention discovery.

The role of engagement in a tailored web-based smoking cessation program: randomized controlled trial.

Background Web-based programs for health promotion, disease prevention, and disease management often experience high rates of attrition. There are 3 questions which are particularly relevant to this issue. First, does engagement with program content predict long-term outcomes? Second, which users are most likely to drop out or disengage from the program? Third, do particular intervention strategies enhance engagement? Objective To determine: (1) whether engagement (defined by the number of Web sections opened) in a Web-based smoking cessation intervention predicts 6-month abstinence, (2) whether particular sociodemographic and psychographic groups are more likely to have lower engagement, and (3) whether particular components of a Web-based smoking cessation program influence engagement. Methods A randomized trial of 1866 smokers was used to examine the efficacy of 5 different treatment components of a Web-based smoking cessation intervention. The components were: high- versus low-personalized message source, high- versus low-tailored outcome expectation, efficacy expectation, and success story messages. Moreover, the timing of exposure to these sections was manipulated, with participants randomized to either a single unified Web program with all sections available at once, or sequential exposure to each section over a 5-week period of time. Participants from 2 large health plans enrolled to receive the online behavioral smoking cessation program and a free course of nicotine replacement therapy (patch). The program included: an introduction section, a section focusing on outcome expectations, 2 sections focusing on efficacy expectations, and a section with a narrative success story (5 sections altogether, each with multiple screens). Most of the analyses were conducted with a stratification of the 2 exposure types. Measures included: sociodemographic and psychosocial characteristics, Web sections opened, perceived message relevance, and smoking cessation 6-months following quit date. Results The total number of Web sections opened was related to subsequent smoking cessation. Participants who were younger, were male, or had less formal education were more likely to disengage from the Web-based cessation program, particularly when the program sections were delivered sequentially over time. More personalized source and high-depth tailored self-efficacy components were related to a greater number of Web sections opened. A path analysis model suggested that the impact of high-depth message tailoring on engagement in the sequentially delivered Web program was mediated by perceived message relevance. Conclusions Results of this study suggest that one of the mechanisms underlying the impact of Web-based smoking cessation interventions is engagement with the program. The source of the message, the degree of message tailoring, and the timing of exposure appear to influence Web-based program engagement.

Statistical methods for dynamic treatment regimes

Statistical methods for dynamic treatment regimes : , Statistical methods for dynamic treatment regimes : , کتابخانه دیجیتال جندی شاپور اهواز

Inference for non-regular parameters in optimal dynamic treatment regimes

A dynamic treatment regime is a set of decision rules, one per stage, each taking a patient’s treatment and covariate history as input, and outputting a recommended treatment. In the estimation of the optimal dynamic treatment regime from longitudinal data, the treatment effect parameters at any stage prior to the last can be non-regular under certain distributions of the data. This results in biased estimates and invalid confidence intervals for the treatment effect parameters. In this article, we discuss both the problem of non-regularity, and available estimation methods. We provide an extensive simulation study to compare the estimators in terms of their ability to lead to valid confidence intervals under a variety of non-regular scenarios. Analysis of a data set from a smoking cessation trial is provided as an illustration.

Developing multicomponent interventions using fractional factorial designs

Multicomponent interventions composed of behavioral, delivery, or implementation factors in addition to medications are becoming increasingly common in health sciences. A natural experimental approach to developing and refining such multicomponent interventions is to start with a large number of potential components and screen out the least active ones. Factorial designs can be used efficiently in this endeavor. We address common criticisms and misconceptions regarding the use of factorial designs in these screening studies. We also provide an operationalization of screening studies. As an example, we consider the use of a screening study in the development of a multicomponent smoking cessation intervention. Simulation results are provided to support the discussions.

Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions

Background Many interventions in todays health sciences are multicomponent, and often one or more of the components are behavioral. Two approaches to building behavioral interventions empirically can be identified. The more typically used approach, labeled here the classical approach, consists of constructing a likely best intervention a priori, and then evaluating the intervention in a standard randomized controlled trial (RCT). By contrast, the emergent phased experimental approach involves programmatic phases of empirical research and discovery aimed at identifying individual intervention component effects and the best combination of components and levels. Purpose The purpose of this article is to provide a head-to-head comparison between the classical and phased experimental approaches and thereby highlight the relative advantages and disadvantages of these approaches when they are used to select program components and levels so as to arrive at the most potent intervention. Methods A computer simulation was performed in which the classical and phased experimental approaches to intervention development were applied to the same randomly generated data. Results The phased experimental approach resulted in better mean intervention outcomes when the intervention effect size was medium or large, whereas the classical approach resulted in better mean intervention outcomes when the effect size was small. The phased experimental approach led to identification of the correct set of intervention components and levels at a higher rate than the classical approach across all conditions. Limitations Some potentially important factors were not varied in the simulation, for example the underlying structural model and the number of intervention components. Conclusions The phased experimental approach merits serious consideration, because it has the potential to enable intervention scientists to develop more efficacious behavioral interventions. Clinical Trials 2009; 6: 5—15. http://ctj.sagepub.com

Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme

A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.

Screening Experiments and the Use of Fractional Factorial Designs in Behavioral Intervention Research

Health behavior intervention studies have focused primarily on comparing new programs and existing programs via randomized controlled trials. However, numbers of possible components (factors) are increasing dramatically as a result of developments in science and technology (e.g., Web-based surveys). These changes dictate the need for alternative methods that can screen and quickly identify a large set of potentially important treatment components. We have developed and implemented a multiphase experimentation strategy for accomplishing this goal. We describe the screening phase of this strategy and the use of fractional factorial designs (FFDs) in studying several components economically. We then use 2 ongoing behavioral intervention projects to illustrate the usefulness of FFDs. FFDs should be supplemented with follow-up experiments in the refining phase so any critical assumptions about interactions can be verified.

Tooth loss and dental caries in community‐dwelling older adults in northern Manhattan

OBJECTIVE To examine tooth loss and dental caries by sociodemographic characteristics from community-based oral health examinations conducted by dentists in northern Manhattan. BACKGROUND The ElderSmile programme of the Columbia University College of Dental Medicine serves older adults with varying functional capacities across settings. This report is focused on relatively mobile, socially engaged participants who live in the impoverished communities of Harlem and Washington Heights/Inwood in northern Manhattan, New York City. MATERIALS AND METHODS Self-reported sociodemographic characteristics and health and health care information were provided by community-dwelling ElderSmile participants aged 65 years and older who took part in community-based oral health education and completed a screening questionnaire. Oral health examinations were conducted by trained dentists in partnering prevention centres among ElderSmile participants who agreed to be clinically screened (90.8%). RESULTS The dental caries experience of ElderSmile participants varied significantly by sociodemographic predictors and smoking history. After adjustment in a multivariable logistic regression model, older age, non-Hispanic Black and Hispanic race/ethnicity, and a history of current or former smoking were important predictors of edentulism. CONCLUSION Provision of oral health screenings in community-based settings may result in opportunities to intervene before oral disease is severe, leading to improved oral health for older adults.

Modeling Social Dimensions of Oral Health Among Older Adults in Urban Environments

In both developed and developing countries, population aging has attained unprecedented levels. Public health strategies to deliver services in community-based settings are key to enhancing the utilization of preventive care and reducing costs for this segment of the population. Motivated by concerns of inadequate access to oral health care by older adults in urban environments, this article presents a portfolio of systems science models that have been developed on the basis of observations from the ElderSmile preventive screening program operated in northern Manhattan, New York City, by the Columbia University College of Dental Medicine. Using the methodology of system dynamics, models are developed to explore how interpersonal relationships influence older adults’ participation in oral health promotion. Feedback mechanisms involving word of mouth about preventive screening opportunities are represented in relation to stocks that change continuously via flows, as well as agents whose states of health care utilization change discretely using stochastic transitions. Agent-based implementations illustrate how social networks and geographic information systems are integrated into dynamic models to reflect heterogeneous and proximity-based patterns of communication and participation in the ElderSmile program. The systems science approach builds shared knowledge among an interdisciplinary research team about the dynamics of access to opportunities for oral health promotion. Using “what if” scenarios to model the effects of program enhancements and policy changes, resources may be effectively leveraged to improve access to preventive and treatment services. Furthermore, since oral health and general health are inextricably linked, the integration of services may improve outcomes and lower costs.